Abstract
Hydrologic models are important tools for the successful management of water resources. In this study, a semi-distributed soil and water assessment tool (SWAT) model is used to simulate streamflow at the headwater of Çarşamba River, located at the Konya Closed Basin, Turkey. For that, first a sequential uncertainty fitting-2 (SUFI-2) algorithm is employed to calibrate the SWAT model. The SWAT model results are also compared with the results of the radial-based neural network (RBNN) and support vector machines (SVM). The SWAT model performed well at the calibration stage i.e., determination coefficient (R2) = 0.787 and Nash–Sutcliffe efficiency coefficient (NSE) = 0.779, and relatively lower values at the validation stage i.e., R2 = 0.508 and NSE = 0.502. Besides, the data-driven models were more successful than the SWAT model. Obviously, the physically-based SWAT model offers significant advantages such as performing a spatial analysis of the results, creating a streamflow model taking into account the environmental impacts. Also, we show that SWAT offers the ability to produce consistent solutions under varying scenarios whereas it requires a large number of inputs as compared to the data-driven models.
Highlights
The studies of hydrological modelling play a crucial role in planning water resources, projecting hydraulic structures, and evaluating environmental impacts [1,2,3,4]
Streamflow estimation studies are required for hydrological assessment, there are some difficulties in implementation
The study area was divided into 87 subbasins including 845 hydrological units (HRUs), and the sequential uncertainty fitting-2 (SUFI-2) was used to calibrate 20 parameters
Summary
The studies of hydrological modelling play a crucial role in planning water resources, projecting hydraulic structures, and evaluating environmental impacts [1,2,3,4]. Streamflow estimation studies are required for hydrological assessment, there are some difficulties in implementation. Based models allow the mathematical solution by transferring the nature events to a computer simulation program. These models are suitable tools for analyzing the process and the factors affecting the process, as well as the results in the modeling of hydrological events. A lot of data is needed to transfer the hydrological process to the computer simulation program in physically based models. Data-driven models such as AI, computational intelligence (CI), soft computing (SC), machine learning (ML), and data mining (DM)
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